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A new group decision-making framework based on 2-tuple linguistic complex q-rung picture fuzzy sets
Muhammad Akram1, Ayesha Khan1, Uzma Ahmad1
1Department of Mathematics, University of the Punjab, New Campus, Lahore, Pakistan.
This study introduces novel 2-tuple linguistic complex q-rung picture fuzzy sets (2TLCq-RPFSs) to handle complex decision-making. These sets enhance multi-attribute decision-making by integrating linguistic terms with advanced fuzzy set theory.
Area of Science:
- Decision Sciences
- Fuzzy Set Theory
- Computational Intelligence
Background:
- Multi-attribute decision-making (MADM) is increasingly complex.
- Existing models like complex q-rung picture fuzzy sets (Cq-RPFS) have limitations.
- Linguistic terms offer intuitive evaluation of qualitative data.
Purpose of the Study:
- Introduce the novel 2-tuple linguistic complex q-rung picture fuzzy sets (2TLCq-RPFSs).
- Develop aggregation operators for the new fuzzy set structure.
- Apply the framework to a practical MADM problem.
Main Methods:
- Definition of 2TLCq-RPFSs.
- Development of 2TLCq-RPF weighted averaging/geometric operators.
- Construction of Hamacher-based aggregation operators.
- Investigation of operator properties.
- Application to an optimal machine selection problem.
Main Results:
- The proposed 2TLCq-RPFS model effectively represents two-dimensional information.
- New aggregation operators demonstrate utility in MADM.
- A numerical example validates the practical applicability and effectiveness of the approach.
Conclusions:
- 2TLCq-RPFSs offer a superior framework for complex decision-making compared to Cq-RPFSs.
- The developed aggregation operators provide robust tools for MADM.
- The proposed methodology is effective for real-world decision problems.
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